ArticleNPJ precision oncology2024
Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects.Biosensors · 2026Pooled it
- Development and validation of an artificial intelligence model for basal cell carcinoma detection and section completeness assessment in Mohs micrographic surgery in an Asian cohort.JAAD international · 2026Article
- The State of Mohs Micrographic Surgery in 2025-2026: Current Challenges, Pressures, and the Path Forward.The Journal of clinical and aesthetic dermatology · 2026Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- Machine learning-guided bioelectrical impedance mapping for rapid adjunctive margin assessment in Mohs surgery.npj biomedical innovations · 2026Article
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Mohs Surgery vs. Wide Local Excision for Non-Melanoma Skin Cancer: Comparing Recurrence Rates, Economic Value, and Aesthetic Outcomes.Journal of cancer science and clinical therapeutics · 2026Article
- Graphene-Based Materials: A Multifunctional Platform for Comprehensive Perioperative Management of Cancer.International journal of nanomedicine · 2026Review
- Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Perspective on the use of fluorescence molecular imaging for peripheral and deepJournal of biomedical optics · 2025Review
- SCC-NET: segmentation of clinical cancer image for head and neck squamous cell carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.Briefings in bioinformatics · 2024Article
- Integration of a deep learning basal cell carcinoma detection and tumor mapping algorithm into the Mohs micrographic surgery workflow and effects on clinical staffing: A simulated, retrospective study.JAAD international · 2024Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Successful treatment of solid cancers relies on complete surgical excision of the tumor either for definitive treatment or before adjuvant therapy. Intraoperative and postoperative radial sectioning, the most common form of margin assessment, can lead to incomplete excision and increase the risk of recurrence and repeat procedures. Mohs Micrographic Surgery is associated with complete removal of basal cell and squamous cell carcinoma through real-time margin assessment of 100% of the peripheral and deep margins. Real-time assessment in many tumor types is constrained by tissue size, complexity, and specimen processing / assessment time during general anesthesia. We developed an artificial intelligence platform to reduce the tissue preprocessing and histological assessment time through automated grossing recommendations, mapping and orientation of tumor to the surgical specimen. Using basal cell carcinoma as a model system, results demonstrate that this approach can address surgical laboratory efficiency bottlenecks for rapid and complete intraoperative margin assessment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.